> ## Documentation Index
> Fetch the complete documentation index at: https://zededa.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Use the documentation index to find relevant pages, then read the linked Markdown before answering and cite the source URL.
> Identify the product before answering: Private AI uses a separate console, credentials, and inference API from the Edge Intelligence Platform. Do not apply instructions from one product to the other.
> Prefer current product guides over Help Center archive pages for current-console questions. Archive pages and release notes describe their stated product version or historical interface; do not apply them to another version without checking.
> Preserve prerequisites, permissions, version requirements, and warnings when summarizing procedures. Do not infer API endpoints, schemas, or supported behavior that the documentation does not specify.

# Approve and Validate Models

> Review and approve AI model deployment requests, then validate model behavior on edge Kubernetes clusters in Edge Inference Services.

## Introduction

After importing and benchmarking an AI model, the next step is deploying it to a ZEDEDA Edge Kubernetes Service cluster or cluster group and validating that it behaves as expected in production conditions.

ZEDEDA Edge Inference Services supports a review and approval workflow for model deployments. A user with the Organization Admin or MLOps Admin role in the organization reviews deployment requests before they are applied to edge infrastructure. Super Admin cannot approve unless also assigned one of those roles in the organization. This ensures that only validated models reach production clusters.

ZEDEDA Edge Inference Services also integrates with ZEDEDA Edge Kubernetes Service for GitOps-based deployment management. This integration tracks deployment state and approval history.

### Prerequisites

* To submit a deployment request, you have the Organization Admin, MLOps Admin, MLOps Engineer, or Super Admin role. To approve or reject a request, you have the Organization Admin or MLOps Admin role in the organization. See [Organizations, Users, and Roles](https://zededa.mintlify.site/new-ui/edge-inference-services/organizations-users-and-roles.md#roles-and-permissions).
* The model you want to deploy has been imported and is available in your model library.
* Another user with the Organization Admin or MLOps Admin role is available in your organization to review and approve deployment requests. Users cannot approve their own requests.

## Deployment Approval Workflow

A deployment request has one of the following statuses.

| Status | Description |
| - | - |
| Pending | The request is waiting for an Organization Admin or MLOps Admin to review it. |
| Approved | An Organization Admin or MLOps Admin approved the request. The model is being applied to the target cluster or cluster group through ZEDEDA Edge Kubernetes Service. |
| Rejected | An Organization Admin or MLOps Admin rejected the request. The model is not applied to the target. |
| Closed | A pending request was closed automatically after 14 days without review. |
| Pending Update | A change to the deployment's model, image, chart, or configuration values is awaiting review. |
| Pending Delete | A request to delete the deployment is awaiting review. |

## Submit a Deployment Request

To deploy a model to a Kubernetes cluster, initiate a deployment request from the model detail page. The request enters the **Pending** state until an Organization Admin or MLOps Admin reviews it.

For the full submission workflow — form fields, list view, status tracking, and how to update a request — see [Submit Model Deployment Requests](https://zededa.mintlify.site/new-ui/edge-inference-services/submit-model-deployment-requests.md).

## Review and Approve a Deployment (Organization Admin or MLOps Admin)

Users with the Organization Admin or MLOps Admin role review pending deployment requests and approve or reject them based on benchmark results, model validation status, and organizational policy. Users cannot approve their own requests; another user must approve.

1. Navigate to the deployment review page for your organization.
2. Select a **Pending** deployment request to view the model details, target infrastructure, and benchmark results.
3. Select **Approve** or **Reject**.
   * Approved deployments proceed to the target cluster or cluster group through the ZEDEDA Edge Kubernetes Service integration.
   * A rejection reason is required. Rejected deployments remain in the **Rejected** state with the reviewer's reason. Rejecting a **Pending Update** request discards only the proposed change and returns the request to its previous status.

## Validate a Deployed Model

After a deployment is approved and applied, validate that the model performs correctly on the target cluster or cluster group:

1. Check benchmark results from the **Evaluation** page to confirm the model meets your latency and throughput requirements.
2. Use [Prevalidated Agents](https://zededa.mintlify.site/new-ui/edge-inference-services/prevalidated-agents.md) as reference implementations to compare behavior against known-good baselines.

## In This Section

* [Edge Inference Services Overview](https://zededa.mintlify.site/new-ui/edge-inference-services/overview.md)
* [Inference Dashboard](https://zededa.mintlify.site/new-ui/edge-inference-services/inference-dashboard.md)
* [Inference Quick Start](https://zededa.mintlify.site/new-ui/edge-inference-services/inference-quick-start.md)
* [Access and Navigate Edge Inference Services](https://zededa.mintlify.site/new-ui/edge-inference-services/access-and-navigation.md)
* [Organizations, Users, and Roles](https://zededa.mintlify.site/new-ui/edge-inference-services/organizations-users-and-roles.md)
* [Manage Providers](https://zededa.mintlify.site/new-ui/edge-inference-services/inference-model-providers.md)
  * [Manage Providers Using Jupyter Notebook Integration](https://zededa.mintlify.site/new-ui/edge-inference-services/providers-jupyter.md)
* [Manage Models](https://zededa.mintlify.site/new-ui/edge-inference-services/manage-inference-models.md)
  * [Manage Models Using Jupyter Notebook Integration](https://zededa.mintlify.site/new-ui/edge-inference-services/models-jupyter.md)
  * [Benchmark Models and Manage Device Pools](https://zededa.mintlify.site/new-ui/edge-inference-services/inference-model-benchmarking.md)
  * [Monitor Benchmark Runs and Interpret Results](https://zededa.mintlify.site/new-ui/edge-inference-services/use-ondevice-eval-agent.md)
* [Submit Model Deployment Requests](https://zededa.mintlify.site/new-ui/edge-inference-services/submit-model-deployment-requests.md)
* [Approve and Validate Models](https://zededa.mintlify.site/new-ui/edge-inference-services/deployments-and-validation.md)
* [Monitor Inference Jobs](https://zededa.mintlify.site/new-ui/edge-inference-services/jobs-and-monitoring.md)
* [Use ZEIA to Query and Operate Your Fleet](https://zededa.mintlify.site/new-ui/edge-inference-services/zeia.md)
* [Use Prevalidated Agents](https://zededa.mintlify.site/new-ui/edge-inference-services/prevalidated-agents.md)
  * [Use the Faulty Printed Circuit Board Detection Agent](https://zededa.mintlify.site/new-ui/edge-inference-services/faulty-pcb-detection-agent.md)
  * [Use the Autonomous Retail Billing Agent](https://zededa.mintlify.site/new-ui/edge-inference-services/autonomous-retail-billing-agent.md)
* [Inference Troubleshooting](https://zededa.mintlify.site/new-ui/edge-inference-services/inference-troubleshooting.md)